Moderation of User-Generated Content

ABSTRACT

A system and method for updating and correcting facts that receives proposed values for facts from users and determines a correctness score which is used to automatically accept or reject the proposed values.

This application is a continuation of and incorporates by reference U.S. Application No. 13/098,342 filed Apr. 29, 2011.

FIELD

The present specification relates to moderation of user-generated content. Background

Online information sources are now frequently updated and corrected by users of the information. All kinds of information on-line is editable by any user, such as business directories, maps, community pages, FAQs, and many other types of information. However that creates the problem of determining the accuracy of user-provided information. Users may be incorrect either on purpose or because the user is mistaken. For example, in the case of a phone number for a business, a user may wish to direct callers to a competing business and provide the competing business’ phone number. Alternatively, the user may, as a prank, provide a completely unrelated phone number. Such intentionally incorrect information is unreliable and is also referred to as “spam.”

Unreliable information is becoming more and more of a problem. Malicious users can intentionally mislead others using computer programs that repeatedly change the value of a map attribute to an unreliable value, even as other users correct the unreliable value.

In some approaches to assessing accuracy of user-provided information, expert reviewers assess the information. This however is laborious and time consuming. In extreme cases, it would require a reviewer to physically go somewhere to verify information. Under this approach, it may take days or weeks for incorrect or out-of-date information to be updated.

Errors from incorrect and outdated information lead to very frustrating experiences for users. For example, a user may obtain the phone number for a business from an on-line business directory. If that phone number is wrong, the user will not be able to contact the business. If the number is intentionally incorrect, for example the listed phone number is the phone number of a competing business, the user may even be mislead into contacting a business with whom they did not intend to do business.

SUMMARY OF THE INVENTION

In one embodiment, a system and method for updating information about features on maps provided by an online map hosting system allows users and other sources (referred to jointly as “users”) to add map features and update or correct information about attributes of map features that appear on maps. Users propose edits from a client device such as a personal computer or a mobile client device such as a smartphone, PDA, laptop, or the like.

From a probability that a source provides unreliable information and other factors, the system determines a score indicating the measure of the correctness of the proposed information. The correctness score is further used by the system to moderate the submitted information automatically. Information determined to have a sufficiently high score is published automatically without review by a human reviewer.

A map editing system comprises a network interface configured to receive proposed values for attributes of map features from client devices. The map editing system further comprises engines (equivalently, “modules”) to automoderate edits.

The features and advantages described in this summary and the following detailed description are not all-inclusive. Many additional features and advantages will be apparent to one of ordinary skill in the art in view of the drawings, specification, and claims hereof.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a diagram of a system architecture according to one embodiment.

FIG. 2 illustrates the interrelation between the different computed values in determining the consensus value according to one embodiment.

FIG. 3 is a data flow chart illustrating the process of determining the consensus value for an attribute of a feature according to one embodiment.

FIG. 4 is a data flow chart illustrating the determination of a session user unreliability score according to one embodiment.

FIG. 5 is a data flow chart illustrating the determination of an edit user unreliability score according to one embodiment.

FIG. 6 illustrates the determination of the value correctness according to one embodiment.

FIG. 7 is a data flow chart illustrating the operation of the overclustering engine according to one embodiment.

DETAILED DESCRIPTION Introduction

An online map is one example of an online information source that makes information about specific facts available to user. An online map includes a plurality of map features, each of which has multiple attributes. A feature and its attributes correspond to a fact which is extrinsically verifiable. A map feature is anything that might appear on a map that would be of interest to those using a map. Map features include, but are not limited to, natural features such as bodies of water, mountains, forests, rivers, and so forth; man-made features, such as cities, addresses, streets, businesses, airports, train stations, stadiums, ports, buildings; and points of interest (POI) such as historic sites, and landmarks. Attributes of map features are characteristics of the map feature that identify, locate, or describe the feature, and include, for example, the latitude and longitude of the map feature, and the name of the feature. Additional attributes are appropriate depending on the type of map feature. Street address is an attribute of many types of map features. Attributes of map features that are businesses include phone numbers, reservations phone numbers, customer service phone numbers, fax numbers, website address, email address and opening times. Generally, attributes are any pieces of information about a map feature that are useful to users of maps, and can describe the physical, contextual, or semantic aspects of a map feature.

Edits to the map are additions and updates to the online map. Edits include the addition of a map feature not previously on the map and corrections and updates to attributes of map features that are present. Edits are received by a map editing server and come from a variety of sources. Databases of map features can be purchased by the online map provider. Information from such databases can be used to add map features not previously on the map or update the attributes of map features already part of the online map. Additionally, individual users can propose map features as well as propose updates to attributes of map features.

From the received proposed values and metadata from the user providing the proposed value, the system determines a number of scores: the probability that a proposed value is accurate, the probability that the user proposing the value provides accurate information, the probability that the user who provided the value provides unreliable information, and the probability that the proposed value is unreliable. Additionally, the system determines a consensus value for the attribute, automatically accepts and rejects proposed values based on a score indicating the probability that the value is correct and reviews map features to determine whether two map features that are actually two separate map features were improperly merged after a mistaken determination that the two map features were duplicates of eachother.

Architecture

FIG. 1 is a diagram of system architecture according to one embodiment. A map editing server 100 communicates with clients 155 and an online map hosting system 130 via a network interface 105 and a network 150.

The map editing server 100 comprises a consensus engine 110, an accuracy engine 120, a reliability engine 125, a user profile database 135, a map feature database 140, edit logs 145, an overclustering engine 160 and an automoderation engine 165. For simplicity, only one map editing server 100, consensus engine 110, accuracy engine 120, reliability engine 125, user profile database 135, map feature database 140, edit logs 145, overclustering engine 160 and automoderation engine 165 are shown but in practice many of each of these components may be in operation.

The map editing server 100 is implemented on one or more server-class computers comprising a CPU, memory, network interface, peripheral interfaces, and other well known components, and executing under the control of one or more server programs. The computers themselves preferably run an open-source operating system such as LINUX, have generally high performance CPUs, with 1G or more of memory, and 100G or more of disk storage. Of course, other types of computers can be used, and it is expected that as more powerful computers are developed in the future, they can be configured in accordance with the teachings here. The functionality implemented by any of the elements can be provided from computer program products that are stored in tangible, non-transitory computer accessible storage mediums (e.g., RAM, hard disk, or optical/magnetic media), or by equivalent implementations in hardware and/or firmware.

The network 150 is typically the Internet, but may also be any network, including but not limited to a LAN, a MAN, a WAN, a mobile, wired or wireless network, telecommunication network, a private network, or a virtual private network, and any combination thereof.

The client 155 is any type of device that is adapted to access the map editing server 100 and online map hosting system 130 over the network 150 and that allows user to input information which the client 155 in turn transmits to the map editing server 100. Examples include, but are not limited to, personal computing devices including handheld and mobile devices. Users access online maps from the online map hosting system 130 (described below), and propose edits to the map via the client 155. The proposed edits are received at the map editing server 100 via the network interface 105. The map editing server 100 provides updated map data to the online map hosting system 130. For simplicity only three clients 155 are shown; in practice there will be numerous clients 155 communicating with map editing server 100.

The online map hosting system 130 is any web-based application known in the art that provides online maps and information about map features, such as business listings, to users. An exemplary online map hosting system 130 is GOOGLE™ Maps. Upon receiving updated map data from the map editing server 100, the online map hosting system 130 can incorporate the updated map data in maps provided by the online map hosting system 130.

The consensus engine 110, accuracy engine 120 and reliability engine 125 are part of the process of determining a consensus value for an attribute of a map feature. Referring to FIG. 2 in addition to FIG. 1 , a description of these components and an overview of the values determined by the engines is provided. The determination of consensus values is an iterative process where the component scores are determined multiple times until each of the scores converges. Then the consensus value is determined. The determination of each score and the consensus value is described in greater detail after this overview. The accuracy engine 120 and reliability engine 125 each provide input to the consensus engine 110 which in turn determines the consensus value 225 for the attribute. The accuracy engine 120 determines a score (“user accuracy 215”) indicative of how often the values proposed by a given user are accurate for the attribute for which they are proposed. A user has a high user accuracy score 215 when the values the user proposes have high probabilities of being the correct value.

The reliability engine 125 determines a score (“value unreliability 210”) indicative of the probability a proposed value is unreliable, which is based on the value probability 220, and is one means for performing this function. The higher the value unreliability 210, the more likely the proposed value is unreliable, or spam. The value unreliability 210 takes into account all instances of a particular value being proposed regardless of the feature for which the value is being proposed. For example, the value unreliability 210 of the phone number 312-555-1212 is determined regardless of whether that phone number is being proposed for a pizza parlor, a library or a movie time information line.

Additionally, the reliability engine 125 determines a score indicative of the probability that each user having proposed a value provides unreliable information. The higher the user unreliability score, the more likely that the user is unreliable or a spammer. In one embodiment, the reliability engine 125 determines the probability that a user provides unreliable information according to one or more of three different models. A first model is based on the iterative model used to determine a consensus value for an attribute and is the consensus value user unreliability 211. The consensus value user unreliability 211 is optionally used by the consensus engine 110 in the determination of the value probability 220. The operation of the of reliability engine 125 using the first model is described in greater detail in reference to FIG. 3 .

A second model is based on analysis of user’s editing sessions and is the session-based user unreliability 411. The operation of the reliability engine 125 using the second model is described in greater detail in reference to FIG. 4 .

A third model is based on analysis of characteristics of the values proposed by the user and is the content-based user unreliability 511. The operation of the reliability engine 125 using the third model is described in greater detail in reference to FIG. 5 .

In one embodiment, the three scores, 211, 411 and 511 are combined into a comprehensive user unreliability score. In other embodiments the scores from only one or two of the models are used.

The consensus engine 110 determines the value probability 220 which is the probability that a given value is the correct value for the attribute for which it is proposed. The value probability 220 is based on the user accuracy 215 of the user proposing the value and optionally on the value unreliability 210 as well. The value probabilities 220 for the values proposed for an attribute are analyzed by the consensus engine 110 to determine the consensus value 225 for the attribute. The operation of each of these engines is discussed in further detail below.

The consensus value user unreliability 211, value unreliability 210, user accuracy 215, value probability 220, and consensus value 225 are determined regularly in an iterative process. As the iterative process proceeds until the determined scores individually stabilize or converge.

The automoderation engine 165 automatically accepts or rejects proposed values for attributes using scores determined by the accuracy engine 120, reliability engine 125 and consensus engine 110, and is one means for performing this function. The operation of the automoderation engine is described in greater detail below in reference to FIG. 6 .

The overclustering engine 160 identifies map feature records which could be multiple map features mistakenly combined into a single record and flags these map feature records for additional review, and is one means for performing this function. The overclustering engine 160 uses edit correctness 605 for the attributes of map features as determined by the automoderation engine 165. The operation of the overclustering engine 160 is described in greater detail in reference to FIG. 7 .

The user profile database 135 stores profiles for users of the map editing server 100 including for each user, a user ID, the user accuracy 215, user unreliability scores 211, 411 and 511 and reference(s) to the edits proposed by the user, and in the case of edits that are proposing values for attributes of map features, the value probability 220 for the proposed values stored in the map feature database 140.

The map feature database 140 stores the edits proposed by users. For those edits which are proposed values for attributes of map features, the value unreliability 210 and value probability 220 for the proposed values is stored as associated with the edit. The map feature database 140 additionally stores the overclustering score 713 for map features and the edit correctness 605.

The edit logs 145 contain information about user interactions with the map editing server 100. Each log entry includes timestamp information, the user’s ID, the type of interaction and other metadata. In one embodiment, the edit logs 145 also include user interactions with the online map hosting system 130. Optionally, edit logs 145 include an IP address of the client device interacting with the map editing server 100.

Determining Consensus Values for Attributes of Map Features

FIG. 3 illustrates an interaction diagram showing one embodiment of a process for determining the consensus value 225 for an attribute of a feature. Users input proposed values at their respective clients 155 which transmit them to the map editing server 100. The proposed values are received 305 by the map editing server 100 and stored in the map feature database 140. In one embodiment, a proposed value is in the form of the statement [u:e=x], user u proposes that attribute e has value x. The statement can also be represented as a 3-tuple: (u, e, x). The proposed value can be transmitted in the form of a message to the map editing server 100, for example passing the components u, e, and x as parameters via a web page or web service. More generally, the map editing server 100 exposes an application programming interface (API) by which users can input their proposed values, as well as make other requests.

The consensus engine 110 determines 315 the value probabilities 220 for the proposed values for an attribute. In order to determine 315 value probabilities 220 for the proposed values for an attribute, the consensus engine 110 retrieves 307 a subset of proposed values for the attribute as well as the value unreliability 210 for each from the map feature database 140. The consensus engine 110 also retrieves 311 the user accuracy 215 for the user providing each proposed value from the user profile database 135. These inputs are used to determine 315 the value probability 220.

Using a Bayesian model, value probability 220, s, for each proposed value x, is determined as follows:

$s = \Pr\left( g(e) = x \middle| A(e) \right) = \frac{\overline{s}(x) \cdot \prod_{u \in U{(e)}}\Pr\left( \left\lbrack {u:e = y} \right\rbrack \middle| q_{u},x \right)}{\sum_{x^{\prime}}\overline{s}\left( x^{\prime} \right) \cdot \prod_{u \in U{(e)}}\Pr\left( \left\lbrack {u:e = y} \right\rbrack \middle| q_{u},x^{\prime} \right)}$

wherein:

-   s(x) is the a priori probability that x is the correct value -   q_(u) is the user accuracy 215. When a user is new to the system,     q_(u) is set to a default. As the user proposes more values for     attributes, q_(u) is adjusted based on actual information about     values proposed by the user. -   A(e) is the set of statements made for the attribute, e -   U(e) is the set of all users who proposed values for e -   X(e) is the set of all proposed values, x, for e -   u(e) is the proposed value provided by a user for e -   g(e) is the true state of e

In embodiments where value unreliability 210, z_(x), is taken into account, s(x) is substituted by

$\overline{s}(x) \propto \frac{1 - z_{x}}{n_{e}}$

wherein n_(e) is the total number of different values that have been proposed for e. The value probabilities 220 are then stored 317 in the map feature database 140.

In an alternative embodiment, value probability 220 is determined through a voting inference when the user accuracy 215, q_(u), is used as a weight for the proposed value provided by the user. Each user is essentially voting on the proposed value provided by the user for an attribute with the user’s own history. This embodiment accounts for the fact that the values proposed by users are not statistically independent. Using voting inference, the value probability 220 is determined as follows:

$\text{s} = \frac{\sum_{\text{u} \in \text{U}{(\text{e})}|\text{u}{(\text{e})} = \text{x}}\text{f}\left( \text{q}_{\text{u}} \right)}{\sum_{\text{u} \in \text{U}{(\text{e})}}\text{f}\left( \text{q}_{\text{u}} \right)}$

wherein f(q_(u)) is an amount of support the proposed value has which can be the identity function or alternatively, it may be a non-negative function representing the cost, for a user, of achieving accuracy q.

In yet another alternative, the Bayesian and voting approaches are combined giving the following determination of the value probability 220:

$s = r(x) \cdot \frac{\sum_{u \in U{(e)}|u{(e)} = x}f\left( q_{u} \right)}{\sum_{u \in U{(e)}}f\left( q_{u} \right)}$

wherein r(x) is an existence probability for each proposed value, x and is determined as follows:

$r(x) = \frac{\alpha \cdot \overline{s}(x) \cdot \prod_{u \in U{(e)}|u{(e)} = x}\left( {1 + f\left( q_{u} \right)} \right)}{1 + \alpha \cdot \overline{s}(x) \cdot \prod_{u \in U{(e)}|u{(e)} = x}\left( {1 + f\left( q_{u} \right)} \right)}$

and wherein α is an attenuation factor and has the value 0 < α ≤ 1 and the value unreliability 210, z_(x), is taken into account by substituting s(x) with

$\overline{s}(x) \propto \frac{1 - z_{x}}{n_{e}}.$

To determine 325 the value unreliability 210, the reliability engine 125 retrieves 319 from the map feature database 140 the instances that a particular value is proposed for any attribute as well as the value probability 220 for each of those instances that the value was proposed. For example, assume that a user provides as proposed value for the phone number of a particular business the string “312-555-1212”. All instances of “312-555-1212” being proposed as the phone number for any feature that has a phone number is requested as well as the value probability 220 for each instance of “312-555-1212” being proposed. The reliability engine 125 determines 325 the value unreliability 210 for the proposed value and stores 327 it in the map feature database 140.

In one embodiment, the reliability engine 125 determines 325 value unreliability 210, z_(x), for a value, x, as follows:

$\text{z}_{\text{x}} = \Pr\left( \tau \middle| s_{1},\mspace{6mu}\ldots\mspace{6mu},s_{n} \right) = \frac{\Pr(\tau) \cdot \prod_{i = 1}^{n}Pr\left( s_{i} \middle| \tau \right)}{\sum_{\tau^{\prime} \in T}\Pr\left( \tau^{\prime} \right) \cdot \prod_{i = 1}^{n}Pr\left( s_{i} \middle| \tau^{\prime} \right)}$

wherein s is the value probability 220, τ is the reliability type, T={τ_(h),τ_(s)}, τ_(h) represents a reliable type value and τ_(s) represents an unreliable type value.

To determine 339 user accuracy 215, the accuracy engine 120 retrieves 335 the value probabilities 220 for proposed values provided by the user. The accuracy engine 120 then determines 339 the user accuracy 215. In one embodiment, the user accuracy 215 is the average of the value probabilities 220 for all of the proposed values provided by the user. Optionally, the accuracy engine 120 requests value probabilities 220 for only a subset of proposed values provided by a user. In such an embodiment, the accuracy engine 120 may only request value probabilities 220 for proposed values provided recently such as for example in the last month, six months or a year. In yet another alternative, the value probabilities 220 for values proposed by the user are weighted based on the elapsed time since the user proposed that value with value probabilities for more recent proposed values weighted more heavily.

In one embodiment, to determine user accuracy 215, the value probabilities 220 for proposed values provided by the user are recalculated without the data provided by that user. These value probabilities 220 are used in the determination of user accuracy 215. Removing the user’s own data from the determination of value probabilities 220 for values the user has proposed removes a self-congratulatory effect where a user’s own submissions could inflate the user’s accuracy score. The user accuracy 215 is stored 341 in the user profile database 135.

To determine consensus value user unreliability 211, the reliability engine 125 retrieves 343 the value unreliabilities 210 for proposed values provided by the user. The reliability engine 125 then determines 347 the consensus value user unreliability 211. The user unreliability is determined using Equation 5 with τ is the reliability type, T={τ_(h),τ_(s)}, τ_(h) represents a reliable type user and τ_(s) represents an unreliable type user The consensus value user unreliability 211 is stored 349 in the user profile database 135.

The determined value probabilities 220 are used for a probability distribution over the subset of proposed values, x, which in turn is used to determine the consensus value 225. The consensus value 225 is the value whose value probability 220 is at the top of the peak of the distribution. Consensus value 225 is determined after the value probability and its inputs, user accuracy and optionally, value unreliability and user unreliability, have been iterated multiple times and those values are converging.

The value probabilities 220 are used for a probability distribution over the subset of proposed values which in turn is used to determine 331 the consensus value 225. The consensus engine 110 stores 333 the determined consensus value 225 in the map feature database 140 as the value for the attribute for which it was proposed and optionally provides 334 the consensus value 225 to the online map hosting system 130. The online map hosting system 130 then displays the consensus value 225 as the value for the attribute on maps provided to users.

Example 1

Table 1 shows a dataset of phone numbers proposed for three features: Pizza House, Hair Salon and Flower Shop by 5 users, A, B, C, D and E. A has proposed the same phone number for all three features. A is probably a spammer and thus an unreliable user and the value 312-555-1212 is likely an unreliable value. Of the other four proposed values for the phone number for Pizza House, three are the same and one is off by one digit from the three that are the same. It is likely that user E just made a mistake or a typo when proposing the phone number for Pizza House rather than purposely proposing the wrong phone number. The disclosed methods make these determinations automatically. The example shows the method through multiple iterations.

TABLE 1 Statements of Users Feature Attribute Value Users Flower Shop 312-555-1212 A Flower Shop 312-256-3636 D, E Hair Salon 312-555-1212 A Pizza House 312-555-1212 A Pizza House 312-749-9992 B, C, D Pizza House 312-749-9996 E

In this example no information is yet known about these users and an a priori user accuracy is set. The a priori user accuracy is programmed and in this example is set to 0.7. The value probabilities 220 are determined using the a priori user accuracy 215 and are shown in Table. 2.

TABLE 2 First Iteration Value Probabilities Feature Attribute Value Value probability Flower Shop 312-555-1212 0.1500 Flower Shop 312-256-3636 0.8500 Hair Salon 312-555-1212 1.0000 Pizza House 312-555-1212 0.0152 Pizza House 312-749-9992 0.9697 Pizza House 312-749-9996 0.0152

The value probabilities are added back to the model to determine calculated user accuracies 215, shown in Table 3. In this embodiment, user accuracy 215 is capped at 0.9500. User A who is likely a spammer has a lower user accuracy. User E does as well.

TABLE 3 First Iteration User Accuracies User User Accuracy A 0.0826 B 0.9500 C 0.9500 D 0.9098 E 0.4326

The calculated user accuracies 215 are added back into the model to determine the value unreliability 210 as shown in Table 4. The spam phone number 312-555-1212 has a high unreliability score. The two numbers that have high value probabilities 220 as correct numbers for Pizza House and Flower Shop, have a 0 score for value unreliability 210.

TABLE 4 First Iteration Value Unreliabilities Value Value Unreliability 312-256-3636 0.0000 312-749-9996 0.0020 312-749-9992 0.0000 312-555-1212 0.0446

Consensus value user unreliabilities 211 are determined and shown in Table 5. The highest score is for user A who proposed the same phone number for three different businesses.

TABLE 5 First Iteration User Unreliabilities User User Unreliability A 0.0446 B 0.0000 C 0.0000 D 0.0000 E 0.0010

The system iterates the values a second time. Using user accuracies 215 determined in Table 3 the value probability 220 is determined again and the results are shown in Table 6.

TABLE 6 Second Iteration Value Probabilities Feature Attribute Value Value probability Flower Shop 312-555-1212 0.0206 Flower Shop 312-256-3636 0.9794 Hair Salon 312-555-1212 1.0000 Pizza House 312-555-1212 0.0000 Pizza House 312-749-9992 1.0000 Pizza House 312-749-9996 0.0000

The second iteration of value probabilities 220 are added back in to system for determining a second iteration of user accuracies shown in Table 7. The user accuracy 215 for user A, the spammer, is decreasing.

TABLE 7 Second Iteration User Accuracies User User Accuracy A 0.0103 B 0.9500 C 0.9500 D 0.9500 E 0.4897

Using the second iteration user accuracies 215, second iteration value unreliabilities 210 are determined and are shown in Table 8. The two numbers that were provided only for the businesses for which they are the phone number 312-256-3636 for Flower Shop and 312-749-9992 for Pizza House still have a 0 score indicating that they are reliable as opposed to unreliable. The value unreliability 210 for 312-555-1212, the spam number, has risen.

TABLE 8 Second Iteration Value Unreliabilities Value Spam Probability 312-256-3636 0.0000 312-749-9996 0.0026 312-749-9992 0.0000 312-555-1212 0.1567

Using second iteration values, the consensus value user unreliabilities 211 are determined again. The spammer, User A, has the highest consensus value user unreliability 211 score.

TABLE 9 Second Iteration User Unreliabilities User User Unreliability A 0.1567 B 0.0000 C 0.0000 D 0.0000 E 0.0013

Consensus values 225 after this second iteration are 312-256-3636 as the phone number for Flower Shop; 312-555-1212 for Hair Salon and 312-749-9992 for Pizza House.

Example 2

In an alternative embodiment, the system determines a consensus value 225 without assessing value unreliability 210 and consensus value user unreliability 211. The process proceeds at first as in Example 1.

TABLE 10 Statements of Users Features Attribute Value Users Flower Shop 312-555-1212 A Flower Shop 312-256-3636 D, E Hair Salon 312-555-1212 A Pizza House 312-555-1212 A Pizza House 312-749-9992 B, C, D Pizza House 312-749-9996 E

An a priori user accuracy is used again and set to 0.7. The value probabilities are determined using the a priori user accuracy and are shown in Table. 11. These are the same as in Example 1 because the value unreliability 210 has not entered the calculation yet.

TABLE 11 First Iteration Value Probabilities Feature Attribute Value Value probability Flower Shop 312-555-1212 0.1500 Flower Shop 312-256-3636 0.8500 Hair Salon 312-555-1212 1.0000 Pizza House 312-555-1212 0.0152 Pizza House 312-749-9992 0.9697 Pizza House 312-749-9996 0.0152

The value probabilities are added back to the model to determine calculated user accuracies, shown in Table 12. Because the value unreliability 210 has not entered the calculation yet, these values too are the same as in Example 1. Again, in this example user accuracy 215 is capped at 0.9500.

TABLE 12 First Iteration User Accuracies User User Accuracy A 0.0826 B 0.9500 C 0.9500 D 0.9098 E 0.4326

The system iterates the values a second time. Using user accuracies 215 determined in Table 10 the value probability 220 is determined and the results are shown in Table 13.

TABLE 13 Second Iteration Value Probabilities Feature Attribute Value Value probability Flower Shop 312-555-1212 0.0216 Flower Shop 312-256-3636 0.9784 Hair Salon 312-555-1212 1.0000 Pizza House 312-555-1212 0.0000 Pizza House 312-749-9992 1.0000 Pizza House 312-749-9996 0.0000

The second iteration of value probabilities 220 are added back in to system for determining a second iteration of user accuracies 215 shown in Table 14.

TABLE 14 Second Iteration User Accuracies User User Accuracy A 0.0108 B 0.9500 C 0.9500 D 0.9500 E 0.9318

Consensus values 225 after this second iteration are 312-256-3636 as the phone number for Flower Shop; 312-555-1212 for Hair Salon and 312-749-9992 for Pizza House.

Determining Session-Based User Unreliability

FIG. 4 illustrates the process whereby the reliability engine 125 determines session-based user unreliability score 411. The session-based user unreliability score 411 is based on characteristics of editing sessions identified by analyzing edit sessions of unreliable users, or spammers. Unreliable users are identified through manual moderation of edits. The edit sessions of unreliable users are used as a training set for the reliability engine 125. The following characteristics of edit sessions are used for differentiating unreliable users from reliable users:

-   frequency of editing sessions - This can be determined per day, per     week or per month. Unreliable users tend to be more regular in the     frequency of editing sessions. -   average time between editing sessions - This is measured in days,     weeks or months. Unreliable users tend to edit more frequently. -   mean frequency of editing sessions. -   standard deviation of the frequency of editing sessions. -   number of edits made per session - Unreliable users tend to make     more edits per session than a reliable user. -   amount of time to make an edit - In seconds or minutes. Unreliable     users take very little time to make an edit. -   time of day of the edit session - The day is divided into six     four-hour blocks and the edit sessions are assigned to the four-hour     block in which it occurred. For a user, each time of day receives a     score that is a percentage of the user’s edit sessions taking place     during that four-hour block. Optionally, each edit is assigned to a     block of time and the score is the percentage of individual edits     taking place during that four-hour block. -   whether the edit session is a weekday or a weekend day - This is a     binary score with for example, 0 for weekend editing and 1 for     weekday editing. -   whether the edit session occurred in the evening or during the day -     This is a binary score with for example, 0 for evening editing and 1     for daytime editing.

The reliability engine 125 retrieves 403 edit logs for a user from the edit logs 145. A user’s edits for a given period of time, such as the last day, week, month or multiple months, are retrieved. These edits are divided 405 into editing sessions approximating the periods of time during which the user was making edits. A single session may be determined by a user logging into and out of the map editing server 100. In one embodiment, if a user does not log out manually, the user is deemed to have logged out after some period of non-activity. Alternatively, sessions are determined heuristically, based on the IP addresses and timestamps associated with each edit. By way of example, a session can be fixed in length (e.g., all edits from an IP address within a 30 minute interval from a first edit), or variable (all edits from an IP address, so long as each edit is within X minutes (e.g., 15 minutes) of the previous edit). In one embodiment, interactions with the online map hosting system 130 are also considered when determining edit sessions. For example an interaction with the online map hosting system 130 within X minutes of an edit is a continuation of the session.

Optionally, binary variables may be combined. For example if a session occurs during the day of a weekday, it has a combined score of 2 that goes into the model rather than two scores of 1 each.

The reliability engine 125 analyzes the determined edit sessions to identify 407 for the user for edit session characteristics, and is one means for performing this function. A score is determined for each characteristic and entered into the model to determine 409 the session-based user unreliability 411. In one embodiment, the model generated from the machine learning is a linear regression with multipliers for each measured characteristic. The equation is z_(s) = c_(s) +

$\sum_{i = 1}^{N}c_{i}v_{i}$

wherein v_(i) is the score for each variable, c_(i) is its multiplier and c_(s) is the intercept generated from the model. The session-based user unreliability score 411 is z_(s) normalized on a scale of 0 to 1 with a score closer to 1 indicating a higher probability that the user is unreliable. For example, session-based user unreliability score = exp(z_(s))/(1+exp(z_(s))).

The session-based user unreliability score 411 is stored 413 in the user profile database 135 as associated with the user.

Optionally other actions taken by a user during the edit session are also identified and entered into the model to determine session-based user unreliability 411. Identified actions, or supportive actions, include a user performing a search at a search engine zooming in on a map displayed to the user while the user is making an edit and switching between map and satellite views of a map displayed to the user. Supportive actions are indicative of a reliable user as these are actions a user might take to confirm the information the user is about to provide as an edit. A user intending to spam the system is unlikely to search to make sure the phone number the user is about to propose is the correct phone number or zoom in on a satellite view of the area in which the user is adding a map feature to make sure the position for its location is just right. Supportive actions would be quantified for use in the model as the average number of supportive actions per session, ratio of supportive actions to edits in an edit session, ratio of edit sessions having at least one supportive action and/or ratio of edits that are followed by a supportive action.

Determining Content-Based User Unreliability

Referring to FIG. 5 , the determination of the content-based user unreliability 511 is described. Content-based user unreliability 511 is determined by analyzing edits for weak signaling characteristics that are each individually are mildly suggestive of an unreliable user. One instance of such a signal among a user’s edits is not necessarily dispositive. However, many instances of one of these weak signaling characteristics or many instances of more than one of the weak signaling characteristics can be combined to provide a strong predictive signal of the unreliability of the proposed value. These weak signaling characteristics can be identified by analyzing edit sessions of known unreliable users, or spammers. Manual moderation may also be used to identify unreliable users. The edit sessions of unreliable users are then used as a training set for the reliability engine 125.

In one embodiment, weak signaling characteristics include all capital letters for the name a map feature, the abbreviation “24 HR” (or an analog such as 24 HOUR or 24 hour) in the name of a map feature, the appearance of symbols in names (for example, “$ALE”), names that are very short or very long, the appearance of the name of a city in the name (for example, “San Jose Plumber”), an address that cannot be parsed, an insufficient address (only the city name for example), associations between the edited map feature and a large number of categories, the category names that include the word “cheap.”

The reliability engine 125 retrieves 503 edits made by a user from the edit logs 145. A user’s edits for a given period of time, such as the last day, week, month or multiple months, are retrieved. The reliability engine 125 analyzes the edits to identify 505 the weak signaling characteristics in the edits. In one embodiment, a count is kept incrementing by one for each instance of a weak signaling characteristic among edits of the user. The count for each of the weak signaling characteristics for the user is divided by the total number of edits retrieved and analyzed by the reliability engine 125 resulting in a score for each weak signal. Those scores are combined in a model, for example, a linear regression, with a multiplier for each weak signal score. An example equation is

$z_{c} = c_{c} + \sum_{i = 1}^{N}c_{i}v_{i}$

wherein v_(i) is the score for each weak signal, c_(i) is its multiplier and c_(c) is the intercept generated from the model. The content-based user unreliability score 511 is determined 507 by normalizing z_(c) on a scale of 0 to 1 with a score closer to 1 indicating a higher probability that the user is unreliable. For example, content-based user unreliability score is exp(z_(c))/(1+exp(z_(c)). The content-based user unreliability score is stored 509 in the user profile database 135 as associated with the user.

Auto-Moderation of Map Edits

Referring to FIG. 6 , the operation of the automoderation engine is described. The automoderation engine 165 rejects or accepts edits proposed by users based on the scores 211, 411 and 511 determined for the unreliability of the user proposing the edit, value probability 220, the value unreliability 210, and the user accuracy 215. Alternatively fewer than all of these scores are used by the automoderation engine 165. The automoderation engine 165 determines the edit correctness 605 from the input scores. The model for the determination of the edit correctness 605 is determined through a machine learning classifiers in combination with training data of edits which were manually accepted or rejected. In one embodiment, the model is a linear regression and the equation is

$z_{cor} = c_{cor} + \sum_{i = 1}^{N}c_{i}v_{i}$

wherein v_(i) is the score for each weak signal, c_(i) is its multiplier and c_(cor) is the intercept generated from the model. The edit correctness is z_(cor) normalized on a scale of 0 to 1 with a score closer to 1 indicating a higher probability that the edit is correct. For example, edit correctness 605 is exp(z_(cor))/(1+exp(z_(cor))).

If the edit correctness 605 exceeds a threshold, for example 0.7 or 0.8, the edit is accepted. In one embodiment, the threshold required for accepting an edit differs depending on the prominence, or popularity, of the map feature being edited. The prominence of a map feature is how many impressions it has. An impression occurs when the map feature is displayed to a user on a map or in a list of map feature search results. In one embodiment, edits to map features of high prominence require an edit correctness > 0.9 whereas edits to map feature of low prominence require only an edit correctness > 0.5.

In one embodiment, all edits to a single map feature made by a single user are considered together as one edit. For example, if a user changes the telephone number and title of a map feature, the automoderation engine can treat that as one edit and accept or reject both. In such an embodiment, the edit correctness 605 is determined for each edit. The two edit correctness 605 are then combined (for example as an average) and the acceptance or rejection is determined based on that combined edit correctness 605.

The edit correctness 605 is stored as associated with the edit in the map feature database 140. Those edits that are accepted are optionally provided to the online map hosting system 130 and appear on maps provided to users of the online map hosting system 130.

Overclustering Detection

The operation of the overclustering engine 160 is described in reference FIG. 7 . Overclustering can be detected while map feature records are being created as well as in database of existing database of map feature records. When map feature records are being created, multiple sources of information about map features are frequently consulted. For example, multiple lists of businesses might be purchased. In processing the lists, an attempt is made to identify businesses that appear in both databases and those records are merged. At this point, improper de-duplication can occur. This is especially likely when there are two businesses that share several attributes. For example, there may be a restaurant located inside a hotel. That restaurant is a separate business with its own opening hours, telephone number, etc but it shares the street address of the hotel. The records for the hotel and the restaurant might be incorrectly merged into a single map feature record while processing map feature records from multiple sources. Thus analyzing newly created map feature records for overclustering is useful.

Overclustering may however become evident when the overclustered map feature record is published to the online map hosting system and users propose edits to the map feature. In the example of the hotel and restaurant at the same address, it’s possible that the only business present in the map feature records of the online map hosting system 130 is that for the hotel. Users may propose the phone number for the restaurant inside the hotel as a replacement for or in addition to the hotel’s phone number for the hotel map feature. Users may also propose to change the title of the map feature from the hotel name to the restaurant name.

The overclustering engine 160 retrieves 703 map feature records from the map feature database 140 along with the edit correctness 605 for the values of the attributes of the map feature. The overclustering engine 160 identifies 709 map feature records having characteristics indicative of overclustering. These characteristics are identified by training the overclustering engine 160 with a training data set of records manually identified as overclustered. The characteristics indicative of overclustering include multiple values for more than one of the map feature attributes where each of the values was approved because its edit correctness 605 exceeded the threshold for approving the edit. For example, in reference to the hotel and restaurant example, a map feature record with multiple phone numbers (for example, the main number for the hotel, a reservations number for the hotel, a number for the restaurant) and multiple titles (Bell Tower Hotel and Escoffier Restaurant) would be indicative of overclustering. In one embodiment, any map feature with more than one title wherein the more than one titles have edit correctness scores 605 that exceed the threshold for being accepted are flagged for manual review. When analyzing titles that are both accepted, the system first removes common words from the title like “Inc.” and its variant, “Incorporated.” This way Bell Tower Hotel and Bell Tower Hotel, Inc. are not flagged as overclustered. Similarly, Bell Tower Hotel, Inc. and Bell Tower Hotel Incorporated are not flagged as overclustered.

For the map features flagged as overclustered an overclustering score 713 is determined 711. The overclustering score 713 is based on additional map feature attributes that have multiple values, such as a telephone number. The scoring is based on the model determined by the machine learning of the training data set of overclustered map feature records. The more map feature attributes that have multiple values, the higher the overclustering score 713 for the flagged map feature. Certain attributes contribute more to the overclustering score 713. For example, having multiple phone numbers that are reservation phone numbers is more likely to be overclustered than having multiple unclassified phone numbers. This prioritizes the flagged map features for the manual reviewers such that those most likely to be overclustered will be reviewed first. The overclustering score 713 is stored in the map feature database 140.

The present invention has been described in particular detail with respect to several possible embodiments. Those of skill in the art will appreciate that the invention may be practiced in other embodiments. First, the particular naming of the components, capitalization of terms, the attributes, data structures, or any other programming or structural aspect is not mandatory or significant, and the mechanisms that implement the invention or its features may have different names, formats, or protocols. Further, the system may be implemented via a combination of hardware and software, as described, or entirely in hardware elements. Also, the particular division of functionality between the various system components described herein is merely exemplary, and not mandatory; functions performed by a single system component may instead be performed by multiple components, and functions performed by multiple components may instead performed by a single component.

Some portions of above description present the features of the present invention in terms of methods and symbolic representations of operations on information. These descriptions and representations are the means used by those skilled in the data processing arts to most effectively convey the substance of their work to others skilled in the art. These operations, while described functionally or logically, are understood to be implemented by computer programs. Furthermore, it has also proven convenient at times, to refer to these arrangements of operations as modules or by functional names, without loss of generality.

Unless specifically stated otherwise as apparent from the above discussion, it is appreciated that throughout the description, discussions utilizing terms such as “determining” or “displaying” or the like, refer to the action and processes of a computer system, or similar electronic computing device, that manipulates and transforms data represented as physical (electronic) quantities within the computer system memories or registers or other such information storage, transmission or display devices.

Certain aspects of the present invention include process steps and instructions described herein in the form of an algorithm. It should be noted that the process steps and instructions of the present invention could be embodied in software, firmware or hardware, and when embodied in software, could be downloaded to reside on and be operated from different platforms used by real time network operating systems.

The present invention also relates to an apparatus for performing the operations herein. This apparatus may be specially constructed for the required purposes, or it may comprise a general-purpose computer selectively activated or reconfigured by a computer program stored on a computer readable medium that can be accessed by the computer. Such a computer program may be stored in a tangible computer readable storage medium, such as, but is not limited to, any type of disk including floppy disks, optical disks, CD-ROMs, magnetic-optical disks, read-only memories (ROMs), random access memories (RAMs), EPROMs, EEPROMs, magnetic or optical cards, application specific integrated circuits (ASICs), or any type of media suitable for storing electronic instructions, and each coupled to a computer system bus. Furthermore, the computers referred to in the specification may include a single processor or may be architectures employing multiple processor designs for increased computing capability.

The methods and operations presented herein are not inherently related to any particular computer or other apparatus. Various general-purpose systems may also be used with programs in accordance with the teachings herein, or it may prove convenient to construct more specialized apparatus to perform the required method steps. The required structure for a variety of these systems will be apparent to those of skill in the, along with equivalent variations. In addition, the present invention is not described with reference to any particular programming language. It is appreciated that a variety of programming languages may be used to implement the teachings of the present invention as described herein, and any references to specific languages are provided for invention of enablement and best mode of the present invention.

The present invention is well suited to a wide variety of computer network systems over numerous topologies. Within this field, the configuration and management of large networks comprise storage devices and computers that are communicatively coupled to dissimilar computers and storage devices over a network, such as the Internet, public networks, private networks, or other networks enabling communication between computing systems. Finally, it should be noted that the language used in the specification has been principally selected for readability and instructional purposes, and may not have been selected to delineate or circumscribe the inventive subject matter. Accordingly, the disclosure of the present invention is intended to be illustrative, but not limiting, of the scope of the invention, which is set forth in the following claims. 

1-19. (canceled)
 20. A computing system for moderating user-generated content, the computing system comprising: one or more processors; and one or more non-transitory computer-readable media that collectively store instructions that, when executed by the one or more processors, cause the computing system to perform operations, the operations comprising: obtaining a proposed user-generated content associated with a feature for inclusion in an online database; obtaining metadata associated with a user providing the proposed user-generated content, wherein the metadata comprises information associated with previous user interactions; accessing a reliability engine comprising one or more machine-learned models configured to determine a score indicative of a probability that unreliable information has been provided; providing the proposed user-generated content to the one or more machine-learned models of the reliability engine; receiving as an output of the one or more machine-learned models of the reliability engine, and in response to receipt of the proposed user-generated content, an unreliability score indicative of a probability that the user having proposed the user-generated content has provided unreliable information; and determining to implement an action on the proposed user-generated content based on the metadata associated with the user and the unreliability score as compared to one or more thresholds.
 21. The computing system of claim 20, wherein the metadata is associated with a plurality of stored interactions of the user.
 22. The computing system of claim 20, wherein the metadata associated with the user providing the proposed user-generated content comprises data from edit logs that contain information about user interactions with the online database, each log entry of the edit logs comprising one or more of a timestamp, user ID, and type of interaction.
 23. The computing system of claim 20, wherein determining to implement an action on the proposed user-generated content based on the unreliability score as compared to one or more thresholds comprises automatically accepting or rejecting the user-generated content as part of the online database.
 24. The computing system of claim 20, wherein determining to implement an action on the proposed user-generated content based on the unreliability score as compared to one or more thresholds comprises flagging the user-generated content for manual review.
 25. The computing system of claim 20, wherein edit sessions of unreliable users are used as a training set for training the at least one of the one or more machine-learned models of the reliability engine.
 26. The computing system of claim 20, wherein the proposed user-generated content associated with the feature for inclusion in the online database comprises a new feature to be added to the online database.
 27. The computing system of claim 20, wherein the proposed user-generated content associated with the feature for inclusion in the online database comprises an updated attribute associated with an existing feature already stored in the online database.
 28. The computing system of claim 20, wherein the online database comprises edits from a plurality of sources.
 29. The computing system of claim 20, wherein the one or more machine-learned models are trained with a training dataset generated based on data from one or more manual moderation sessions.
 30. The computing system of claim 29, wherein the training dataset comprises a set of records manually identified as overclustered.
 31. The computing system of claim 20, wherein the online database comprises an online database of community pages.
 32. A computer-implemented method for moderating user-generated content, the method comprising: obtaining, by a computing system comprising one or more computing devices, a proposed user-generated value associated with a feature for inclusion in an online database of features that are accessed from a web-based application; obtaining, by the computing system, metadata associated with a user providing the proposed user-generated value; processing, by the computing system, the proposed user-generated value with a reliability engine to generate a value unreliability score, wherein the reliability engine comprises one or more machine-learned models, wherein the value unreliability score is indicative of a probability that unreliable information has been provided; and determining, by the computing system, to implement an action on the proposed user-generated value based on the value unreliability score as compared to one or more thresholds and based on the metadata associated with the user.
 33. The method of claim 32, wherein the metadata comprises an edit log associated with one or more previous interactions associated with the user.
 34. The method of claim 32, wherein the value unreliability score is determined based on one or more interactions by a second user.
 35. The method of claim 32, wherein the metadata comprises accuracy data associated with one or more previous interactions with the online database.
 36. The method of claim 35, wherein the accuracy data comprises recency weighting that more heavily weights more recently proposed values.
 37. A computing system for moderating user-generated content, the system comprising: one or more processors; and one or more non-transitory computer-readable media that collectively store instructions that, when executed by the one or more processors, cause the computing system to perform operations, the operations comprising: obtaining proposed user-generated content associated with a feature for inclusion in an online database of features; obtaining metadata associated with a user providing the proposed user-generated content, wherein the metadata comprises information associated with previous user interactions with the online database of features; processing the proposed user-generated content with a reliability engine to generate a content-based unreliability score, wherein the reliability engine comprises one or more machine-learned models, wherein the content-based unreliability score is indicative of a probability that unreliable information has been provided; and determining to implement an action on the proposed user-generated content based on the content-based unreliability score as compared to one or more thresholds and based on the metadata associated with the user.
 38. The system of claim 37, wherein the information associated with previous user interactions comprises a frequency of editing sessions.
 39. The system of claim 37, wherein the information associated with previous user interactions comprises a number of edits made per session. 